Method and system for dynamically generating exercise prescription, medium and product

Abstract

The invention discloses a method, system, medium and product for dynamically generating an exercise prescription, and relates to the field of exercise prescription generation. The method comprises the steps of generating an initial exercise prescription of a user through a machine learning model based on historical health records and real-time physiological parameters of the user; determining target motion scene information of a target motion scene of the user according to the current geographical location information of the user; determining target fitness equipment owned by the target exercise scene based on the target exercise scene information; and adjusting the initial exercise prescription according to the target exercise scene information and the target fitness equipment of the user to obtain a current exercise prescription. By implementing the technical scheme provided by the invention, the adaptation degree of the generated exercise prescription and the exercise scene is improved.

Description

Technical Field

[0001] This application relates to the field of exercise prescription generation, and particularly to a method, system, medium, and product for dynamically generating exercise prescriptions. Background Art

[0002] With the continuous improvement of people's attention to a healthy lifestyle, exercise, as a key factor in maintaining physical health, has received increasing attention. Traditional exercise recommendations are often relatively general and cannot meet the huge individual differences in health needs.

[0003] Currently, through machine learning models, the physical function level of users can be accurately identified, and exercise prescriptions adapted to the current physical condition of users can be dynamically generated based on this data.

[0004] However, the generated exercise prescription can only accurately adapt to the current physical condition of the user, and cannot comprehensively consider whether the current exercise scenario where the user is located is suitable for the exercise in the exercise prescription. In the case where the exercise scenario does not match the exercise prescription, the user will not be able to carry out the exercise smoothly according to the exercise prescription in the current exercise scenario, thereby affecting the exercise effect. Summary of the Invention

[0005] This application provides a method, system, medium, and product for dynamically generating exercise prescriptions to improve the adaptability of the generated exercise prescriptions to exercise scenarios.

[0006] In a first aspect, this application provides a method for dynamically generating an exercise prescription. The method includes: generating an initial exercise prescription for the user through a machine learning model based on the user's historical health records and real-time physiological parameters; the historical health records include historical medical records and daily health monitoring data; the daily health monitoring data includes daily physiological parameters, daily sleep time, and daily sleep quality; the real-time physiological parameters include heart rate, blood pressure, and physical fatigue; determining the target exercise scenario information of the user's target exercise scenario according to the geographical location information where the user is currently located; the target exercise scenario information includes the exercise scenario address, exercise scenario type, and environmental information around the exercise scenario; determining the target fitness equipment owned by the target exercise scenario based on the target exercise scenario information; and adjusting the initial exercise prescription according to the user's target exercise scenario information and the target fitness equipment to obtain the current exercise prescription.

[0007] With the above technical solution, by collecting the user's physiological parameters in real time and generating an initial exercise prescription according to the user's real-time physical state, the generated initial exercise prescription can accurately adapt to the user's current physical condition. By determining the exercise scenario where the user is currently located and the fitness equipment they have, and dynamically adjusting the initial exercise prescription based on this data, a current exercise prescription suitable for this exercise scenario is obtained, so that the generated current exercise prescription can accurately adapt to the exercise scenario where the user is currently located. On the premise of ensuring that the generated current exercise prescription can accurately adapt to the user's current physical condition, the adaptability of the generated exercise prescription to the exercise scenario is improved, enabling the user to smoothly carry out exercises according to the current exercise prescription in the exercise scenario where they are currently located and achieving the expected exercise effect.

[0008] Combined with some embodiments of the first aspect, in some embodiments, based on the target exercise scenario information, determining the target fitness equipment owned by the target exercise scenario specifically includes: when the exercise scenario type of the target exercise scenario is indoor, obtaining historical exercise scenario information; the historical exercise scenario information includes the exercise scenario address, exercise scenario type, environmental information around the exercise scenario, fitness equipment owned by the exercise scenario, and the layout diagram of the exercise scenario; determining whether there is a match between the exercise scenario address in the historical exercise scenario information and the exercise scenario address of the target exercise scenario; if so, based on the historical exercise scenario information, obtaining the fitness equipment owned by the exercise scenario corresponding to the target exercise scenario as the target fitness equipment; if not, scanning and identifying the target fitness equipment owned by the target exercise scenario; when the exercise scenario type of the target exercise scenario is outdoor, determining the fitness equipment carried by the user as the target fitness equipment.

[0009] With the above technical solution, when the target exercise scenario is indoor, according to the historical exercise scenario information, similar site past records are preferentially matched to quickly determine the target fitness equipment. In the case of no matching information, the target fitness equipment in the exercise scenario is scanned and identified in real time, saving the time for determining the target fitness equipment and improving the efficiency of generating the exercise plan. When the target exercise scenario is outdoor, determining the fitness equipment carried by the user as the target fitness equipment fully considers the uncertainty and autonomy of the outdoor environment, making the exercise prescription closely fit the user's actual equipment conditions.

[0010] In combination with some embodiments of the first aspect, in some embodiments, when the type of the motion scene of the target motion scene is outdoor, determining that the fitness equipment carried by the user is the target fitness equipment specifically includes: obtaining the motion posture of the user and the pressure information of the user's shoe sole; determining whether the user carries fitness equipment based on the motion posture and the pressure information; if so, determining that the fitness equipment carried by the user is the target fitness equipment based on the historical equipment usage data, the motion posture, and the pressure information; the historical equipment usage data includes historical fitness equipment, the motion postures and the pressure information of the shoe sole when the user carries and uses the historical fitness equipment.

[0011] Adopting the above technical solution, the motion equipment carried by the user is automatically determined according to the motion posture of the user and the pressure information of the shoe sole, without the need for the user to input, greatly improving the intelligence level of generating motion and bringing a convenient experience to the user.

[0012] In combination with some embodiments of the first aspect, in some embodiments, adjusting the initial exercise prescription according to the target motion scene information and the target fitness equipment of the user to obtain the current exercise prescription specifically includes: obtaining the noise decibel range around the user; when the type of the motion scene of the target motion scene is an indoor public sports venue, determining whether the current motion time point is within a preset time range; if so, and the maximum decibel value of the noise decibel range is less than a preset threshold, adjusting the initial exercise prescription based on the first preset noise decibel range, the target motion scene information, and the target fitness equipment to obtain the current exercise prescription; the first preset noise decibel range is the maximum noise range allowed in the target motion scene during normal time periods; if so, and the minimum decibel value of the noise decibel range is greater than the preset threshold, adjusting the initial exercise prescription based on the noise decibel range, the target motion scene information, and the target fitness equipment to obtain the current exercise prescription; if not, adjusting the initial exercise prescription based on the second preset noise decibel range, the target motion scene information, and the target fitness equipment to obtain the current exercise prescription; the second preset noise decibel range is the maximum noise range allowed in the target motion scene outside normal time periods.

[0013] With the above technical solution, when the current exercise time point is within the preset time range, if the highest decibel value within the noise decibel range is less than the preset threshold, it indicates that there are no other users exercising or there are fewer exercising users in the exercise scenario where the user is located. Controlling the noise decibel generated by exercise within the normally permitted noise decibel range can prevent excessive noise generated during exercise from affecting other users around the exercise scenario. When the lowest decibel value within the noise decibel range is greater than the preset threshold, it indicates that there are more exercising users in the exercise scenario where the user is located. Controlling the noise decibel generated by exercise within the currently actually measured noise decibel range can prevent the noise generated during exercise from being greater than the noise generated by other users in the current scenario, thereby affecting the exercise of other users around. When the current exercise time point is not within the preset time range, it indicates that the current time is when other users need to rest. Controlling the noise decibel generated by exercise within the preset noise decibel range can prevent excessive noise generated during exercise from affecting the rest of other users around the exercise scenario.

[0014] In combination with some embodiments of the first aspect, in some embodiments, after the step of adjusting the initial exercise prescription according to the user's target exercise scenario information and the target fitness equipment to obtain the current exercise prescription, the method further includes: based on the target exercise scenario information, the historical health record, and the current exercise prescription, simulating the exercise frequencies of various parts of the user's body during the execution of the current exercise prescription; determining the exercise frequency thresholds of various parts of the user's body according to the historical medical records in the user's historical health record; determining whether there is a part of the body whose exercise frequency exceeds the exercise frequency threshold; if so, adjusting the current exercise prescription.

[0015] With the above technical solution, the exercise frequency thresholds that various parts of the user's body can perform are determined according to the user's historical medical records, comprehensively considering the differences in the exercise frequency thresholds that various parts of the body of users who have been injured or have diseases can perform compared with normal users. At the same time, the exercise frequencies of various parts of the user's body during the execution of the exercise prescription are simulated, and the simulated exercise frequencies of various parts of the body are compared with the exercise frequency thresholds to further determine whether the user's execution of the current exercise prescription will cause damage to various parts of the body. When it is determined that damage may be caused, the exercise prescription is continuously adjusted so that the user's execution of the current exercise prescription will not cause damage to various parts of the body, ensuring the exercise safety of the user during the execution of the current exercise prescription.

[0016] In some embodiments in combination with some embodiments of the first aspect, after the step of adjusting the initial exercise prescription according to the target exercise scenario information and the target fitness equipment of the user to obtain the current exercise prescription, the method further includes: determining whether the user has a disease matching a preset disease according to the historical medical record in the user's historical health record; if so, sending a first reminder message to the user; the first reminder message is used to remind the user to take medicine; predicting a predicted time point when the user needs to take medicine based on the user's real-time physiological parameters, the current exercise prescription and the historical health record; when the current time point is a time point at a preset time interval before the predicted time point, sending a second reminder message to the user; the second reminder message is used to prompt the user to take medicine.

[0017] By adopting the above technical solution, by reminding the user to take medicine, the situation that the user forgets to carry medicine due to negligence is avoided. By predicting the time point when the user takes medicine and sending a reminder message to prompt the user to take medicine when there is still a preset time interval from the predicted time point, the user can arrange the medicine-taking matters in an orderly manner, without always remembering the medicine-taking time or worrying about missing the medicine-taking time, ensuring that all physical indicators of the user can be maintained in a relatively stable state during exercise, enabling good balance between exercise and disease control, and realizing scientific and safe fitness activities.

[0018] In some embodiments in combination with some embodiments of the first aspect, after the step of adjusting the initial exercise prescription according to the target exercise scenario information and the target fitness equipment of the user to obtain the current exercise prescription, the method further includes: calculating the optimal exercise amount required for the user to reach the best sleep state based on the user's daily physiological parameters, daily sleep time, daily sleep quality, current exercise amount and current time point; during the process of the user executing the current exercise prescription, calculating the user's real-time exercise amount according to the user's real-time exercise data and real-time physiological parameters; the real-time exercise data includes exercise type, exercise intensity and exercise duration; the real-time exercise amount is the exercise amount during the process of the user executing the current exercise prescription; when the real-time exercise amount is greater than or equal to the optimal exercise amount, sending a third reminder message to the user; the third reminder message is used to prompt the user not to over-exercise.

[0019] By adopting the above technical solution, it is possible to monitor in real time whether the actual exercise amount of the user exceeds the optimal exercise amount, and when it is monitored that the actual exercise amount of the user exceeds the optimal exercise amount, a prompt message is sent to remind the user not to exercise excessively, so as to avoid the user's body being overly excited due to excessive exercise, which affects subsequent sleep, and enables the body to smoothly enter a good rest state after moderate exercise, thereby helping users with poor sleep quality to gradually improve their sleep quality or enabling users with good sleep quality to continuously maintain good sleep quality, forming a virtuous cycle in which exercise and sleep promote each other, and ensuring that the entire health management process is carried out more scientifically and orderly.

[0020] In a second aspect, an embodiment of the present application provides an exercise prescription generation system, which includes: one or more processors and a memory; the memory is coupled to the one or more processors, and the memory is used to store computer program code, and the computer program code includes computer instructions, and the one or more processors call the computer instructions to cause the exercise prescription generation system to execute the method described in the first aspect and any possible implementation manner in the first aspect.

[0021] In a third aspect, an embodiment of the present application provides a computer-readable storage medium, including instructions, when the above instructions run on the exercise prescription generation system, causing the exercise prescription generation system to execute the method described in the first aspect and any possible implementation manner in the first aspect.

[0022] In a fourth aspect, the present application provides a computer program product, when the computer program product runs on the exercise prescription generation system, causing the exercise prescription generation system to execute the method described in the first aspect and any possible implementation manner in the first aspect.

[0023] It can be understood that the exercise prescription generation system provided in the second aspect above, the storage medium provided in the third aspect, and the computer program product provided in the fourth aspect are all used to execute the method provided by the present application. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding method, and will not be elaborated here.

[0024] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. This application collects the user's physiological parameters in real time and generates an initial exercise prescription according to the user's real-time physical condition, so that the generated initial exercise prescription can accurately adapt to the user's current physical condition. At the same time, by determining the exercise scenario where the user is currently located and the fitness equipment they have, and dynamically adjusting the initial exercise prescription based on this data, a current exercise prescription suitable for this exercise scenario is obtained, enabling the user to achieve the same exercise effect when exercising according to the current exercise prescription as when exercising according to the initial exercise prescription. On the premise of ensuring the exercise effect, the adaptability of the generated exercise prescription to the exercise scenario is improved.

[0025] 2. This application simulates the exercise frequency of each part of the user's body when performing the exercise prescription, compares the simulated exercise frequency of each part of the body with the exercise frequency threshold, further determines whether the user's execution of the current exercise prescription will cause damage to each part of the body, and when it is determined that damage may be caused, continues to adjust the exercise prescription so that the user's execution of the current exercise prescription will not cause damage to each part of the body, ensuring the exercise safety of the user during the execution of the current exercise prescription.

[0026] 3. This application reminds the user to take medicine to avoid the situation where the user forgets to carry medicine due to negligence. At the same time, by predicting the time point when the user takes the medicine and sending a reminder message to prompt the user to take the medicine at a preset time interval before the predicted time point, the user can arrange the taking of medicine in an orderly manner without constantly thinking about the taking time or worrying about missing the taking time, ensuring that the user's physical indicators can be maintained in a relatively stable state during exercise, enabling good balance between exercise and disease control, and ensuring the exercise safety of the user during the execution of the current exercise prescription. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 is a schematic structural diagram of a system architecture to which the method for dynamically generating an exercise prescription in an embodiment of this application can be applied; Figure 2 is a schematic flowchart of the method for dynamically generating an exercise prescription in an embodiment of this application; Figure 3 is another schematic flowchart of the method for dynamically generating an exercise prescription in an embodiment of this application; Figure 4 is an exemplary hardware structural diagram of an exercise prescription generation system in an embodiment of this application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0028] The terms used in the following embodiments of this application are only for the purpose of describing specific embodiments and are not intended to limit this application. As used in the specification and appended claims of this application, the singular forms "a", "an", "the", "above-mentioned", "said", and "this" are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used in this application refers to any or all possible combinations including one or more of the listed items.

[0029] Hereinafter, the terms "first" and "second" are only used for descriptive purposes and cannot be construed as implying or suggesting relative importance or implicitly indicating the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the embodiments of this application, unless otherwise specified, the meaning of "a plurality" is two or more.

[0030] Figure 1 It is a schematic structural diagram of a system architecture to which the method for dynamically generating an exercise prescription in the embodiments of this application can be applied.

[0031] Please refer to Figure 1 , the exercise prescription generation system includes a server, a sensor, and a camera.

[0032] The server, as the core component of the system, is used to generate and adjust exercise prescriptions. The sensor is used to collect real-time physiological parameters of the user and data related to the exercise posture, etc., and transmit this data to the server. The camera is used to collect image information of the exercise scene, etc.

[0033] After receiving the data collected by the sensor, the server will process the data and generate an exercise prescription adapted to the user's current physical condition according to the processed data. After generating the exercise prescription, the server can obtain the fitness equipment in the exercise scene where the user is currently located by identifying the image information collected by the camera, and then dynamically adjust the generated exercise prescription according to the fitness equipment, so that the generated exercise prescription can be adapted to the exercise scene where the user is located.

[0034] Through the above system architecture, the exercise prescription generation system can generate an exercise prescription according to the user's real-time physiological parameters, so that the generated exercise prescription can accurately adapt to the user's current physical condition. At the same time, the exercise prescription is dynamically adjusted according to the exercise scene, so that the generated exercise prescription can also accurately adapt to the exercise scene where the user is currently located.

[0035] In the related art, through a machine learning model, the physical function level of a user can be accurately identified, and based on this data, an exercise prescription adapted to the user's current physical condition can be dynamically generated. However, the generated exercise prescription can only accurately adapt to the user's current physical condition and cannot comprehensively consider whether the current exercise scenario where the user is located is suitable for performing the exercise in the exercise prescription. In the case where the exercise scenario does not match the exercise prescription, the user will not be able to smoothly carry out the exercise according to the exercise prescription in the current exercise scenario, thereby affecting the exercise effect.

[0036] However, by adopting the method for dynamically generating an exercise prescription in the embodiments of the present application, by collecting the physiological parameters of the user in real time and generating an initial exercise prescription according to the user's real-time physical state, after the initial exercise prescription is generated, according to the current exercise scenario where the user is located, the generated initial exercise prescription is dynamically adjusted to obtain the current exercise prescription. On the premise of ensuring that the generated current exercise prescription can accurately adapt to the user's current physical condition, the adaptability of the generated exercise prescription to the exercise scenario is improved, so that the user can smoothly carry out the exercise according to the current exercise prescription in the current exercise scenario and achieve the expected exercise effect.

[0037] The following will be combined with Figure 2 to illustrate the method of the embodiments of the present application.

[0038] Please refer to Figure 2 , which is a schematic flowchart of a method for dynamically generating an exercise prescription in the embodiments of the present application.

[0039] S201. Generate an initial exercise prescription for the user through a machine learning model based on the user's historical health records and real-time physiological parameters.

[0040] Among them, the historical health records include historical medical records, daily health monitoring data, etc. The daily health monitoring data includes daily physiological parameters, daily sleep time, and daily sleep quality, etc. The real-time physiological parameters and daily physiological parameters include heart rate, blood pressure, body fatigue degree, etc.

[0041] Specifically, data acquisition is first performed.

[0042] For the historical health records. The historical medical records of the user are obtained through the medical system or the medical record information uploaded by the user. The historical medical records cover key contents such as the types of diseases the user has suffered from in the past, the time of illness, the treatment process, and the recovery situation. At the same time, the daily heart rate, daily blood pressure, and other daily physiological parameters of the user in daily life are collected in real time through the sensors built in wearable devices (such as sports bracelets, etc.). In addition, by docking with sleep monitoring devices such as smart mattresses and sleep monitoring bracelets, data such as the user's daily sleep time and daily sleep quality are obtained. These devices can accurately record various information such as the user's falling asleep time, waking up time, deep sleep duration, and light sleep duration.

[0043] For real-time physiological parameters, data such as the amplitude, frequency, heart rate, and blood pressure of the user's limb movements are obtained in real time through an acceleration sensor, gyroscope, heart rate sensor, and blood pressure sensor. After obtaining the data, the system will integrate these data according to established algorithms and standards for specific calculations to obtain the real-time physical fatigue level. For example, according to the corresponding normal reference ranges and weight coefficients preset for various indicators such as limb movement amplitude, frequency, and heart rate changes, each indicator is weighted and summed according to its respective weight to finally obtain the physical fatigue level.

[0044] Then, feature information is extracted from the obtained data. First, disease-related features such as the type of disease and the recovery status of the disease are extracted from the historical medical records, and these features are quantified or encoded so that the machine learning model can recognize and process them. Then, features such as the fluctuation range of the heart rate, blood pressure range, sleep time, sleep quality, and the proportion of different exercise types corresponding to the corresponding time are extracted from the daily health monitoring data. Finally, features such as the deviation value and change rate of the user's current heart rate and blood pressure compared to the preset normal range are extracted from the real-time physiological parameters.

[0045] Next, all the extracted feature information is standardized to unify the data format and dimension, remove outliers and noise data, and make the data more standardized.

[0046] Finally, this feature information is used as input features and input into a pre-constructed machine learning model. This machine learning model can be trained based on a large amount of labeled health data and corresponding exercise prescription samples. For example, it is constructed using algorithms such as decision trees and support vector machines in supervised learning or neural networks in deep learning. Based on the input data features, the model outputs an initial exercise prescription for this user through internal complex operations and learning mechanisms. The output initial exercise prescription covers exercise type, exercise intensity, exercise frequency, exercise cycle, exercise duration, etc.

[0047] S202. Determine the target exercise scenario information of the user's target exercise scenario according to the geographical location information where the user is currently located.

[0048] Among them, the target exercise scenario information includes the exercise scenario address, exercise scenario type, environmental information around the exercise scenario, etc. The environmental information around the exercise scenario includes altitude, temperature, humidity, etc.

[0049] Specifically, determine the address and type of sports scene. Obtain the latitude and longitude and other specific coordinate data of the user's current location through the GPS (Global Positioning System) positioning function of the mobile terminal or other smart devices with positioning capabilities. Then use the map application programming interface (API) to input the acquired specific coordinate data into the map system, and determine the sports scene address, address name, and specific area where the sports scene is located of the target sports scene through retrieval and matching of the map database. At the same time, combine the relevant venue classification annotation information in the map database to determine the type of sports scene (such as outdoor parks, indoor stadiums, outdoor stadiums, outdoor fitness trails, indoor gyms, etc.).

[0050] Determine the environmental information around the sports scene. By connecting with a professional geographic surveying and mapping platform, using its high-precision terrain data service, enter the coordinate data and query the corresponding altitude value; or use some map software with terrain altitude display function to directly read the altitude information of the location. At the same time, by calling the meteorological data interface opened by the meteorological department of the specific area where the sports scene is located, enter the coordinate location information corresponding to the sports scene, and obtain real-time temperature and humidity data.

[0051] S203: Determine target fitness equipment for the target sports scene based on the target sports scene information.

[0052] Specifically, we first conduct a preliminary screening based on the determined target sports scene type. If the target sports scene type is a park, we preliminarily determine the target fitness equipment to be common outdoor fitness equipment such as horizontal bars, parallel bars, space walkers, and Tai Chi massagers; if it is an indoor stadium or gym, we preliminarily determine the target fitness equipment to be various barbells, dumbbells, treadmills, spinning bikes, and other equipment suitable for indoor physical training and aerobic exercise; if it is other types of scenes, we preliminarily determine that the target fitness equipment is none.

[0053] Then, the target sports scene address and specific area are combined to further determine the fitness equipment that the target sports scene may have. You can search for the facility list information disclosed by the venue management department or operator on the Internet, or use the detailed annotation information for the sports scene in the map application. Some map software will mark and display the key fitness equipment in some public sports venues. By checking these annotations, you can verify which target fitness equipment exists in the scene.

[0054] In some embodiments, the image recognition function of the smart device can be used to call the existing fitness equipment image database, and by shooting the internal picture of the target sports scene, the specific fitness equipment appearing in the picture can be identified, so as to accurately determine the target fitness equipment owned by the target sports scene.

[0055] S204. Adjust the initial exercise prescription according to the user's target exercise scenario information and target fitness equipment to obtain the current exercise prescription.

[0056] Specifically, first determine the initial exercise equipment required for the user to execute the initial exercise prescription, and judge whether all the equipment in the initial exercise equipment is included in the target fitness equipment.

[0057] If the target fitness equipment includes all the equipment in the initial exercise prescription, adjust the exercise intensity of the initial exercise prescription according to the altitude of the target exercise scenario. According to the preset corresponding table of altitude and exercise intensity, the altitude of the target exercise scenario, and the exercise intensity in the initial exercise prescription, determine whether the target exercise scenario is suitable for performing exercises with the same exercise intensity in the initial exercise prescription. If not, adjust the exercise intensity in the initial exercise prescription to the exercise intensity corresponding to the altitude of the target exercise scenario according to the corresponding table of altitude and exercise intensity.

[0058] If the target fitness equipment does not include all the equipment in the initial exercise prescription, replace the missing fitness equipment, and adjust the initial exercise prescription according to the replaced fitness equipment and the altitude of the target exercise scenario. Based on the exercise types that can be performed by each piece of equipment in the existing target fitness equipment and the exercise types performed by each piece of equipment in the initial exercise prescription, replace the missing exercise equipment in the initial exercise prescription with the target fitness equipment that can perform the same exercise type, and then determine the new exercise intensity and exercise duration according to the replaced equipment and altitude. Finally, re-plan specific exercise parameters such as the number of exercise groups, the number of times per group, and the rest interval according to the new exercise intensity and exercise duration, so as to obtain the current exercise prescription that adapts to the current target exercise scenario and target fitness equipment, enabling the user to achieve the same exercise effect by exercising according to the current exercise prescription as by exercising according to the initial exercise prescription.

[0059] In some embodiments, if the exercise scenario is outdoors, the exercise intensity after adjusting the exercise prescription can be further determined according to the outdoor temperature and humidity.

[0060] In some embodiments, the exercise type after adjusting the exercise prescription can be further determined according to the size of the exercise area of the exercise scenario and the size of the exercise area required for performing the exercise type in the exercise prescription.

[0061] In the embodiments of the present application, by collecting the physiological parameters of the user in real time and generating an initial exercise prescription according to the user's real-time physical state, the generated initial exercise prescription can accurately adapt to the user's current physical condition. By determining the exercise scenario where the user is currently located and the fitness equipment owned by the user, and dynamically adjusting the initial exercise prescription based on this data to obtain the current exercise prescription suitable for the exercise scenario, the generated current exercise prescription can accurately adapt to the exercise scenario where the user is currently located. At the same time, the exercise effect achieved by the user exercising according to the current exercise prescription is the same as that achieved by exercising according to the initial exercise prescription. On the premise of ensuring the exercise effect, the adaptability of the generated exercise prescription to the exercise scenario is improved.

[0062] The following will further illustrate the method of the embodiments of the present application in conjunction with Figure 3 to further illustrate the method of the embodiments of the present application.

[0063] Please refer to Figure 3 , which is another schematic flowchart of the exercise prescription prescribing and prescription execution methods in the embodiments of the present application.

[0064] S301. Generate an initial exercise prescription for the user through a machine learning model based on the user's historical health records and real-time physiological parameters.

[0065] S302. Determine the target exercise scenario information of the user's target exercise scenario according to the geographical location information where the user is currently located.

[0066] Steps S301 and S302 are similar to steps S201 and S202 in the Figure 2 illustrated embodiments. For the descriptions in steps S201 and S202, please refer to them. Details are not described herein again.

[0067] S303. When the exercise scenario type of the target exercise scenario is indoor, obtain the historical exercise scenario information.

[0068] When the exercise scenario type of the target exercise scenario is indoor, obtain the historical exercise scenario information stored in the database.

[0069] Among them, the historical exercise scenario information includes the exercise scenario address, exercise scenario type, environmental information around the exercise scenario, fitness equipment owned by the exercise scenario, and the layout diagram of the exercise scenario, etc. The layout diagram includes the positions of each fitness equipment in the exercise scenario.

[0070] S304. Determine whether there is a match between the exercise scenario address in the historical exercise scenario information and the exercise scenario address of the target exercise scenario.

[0071] According to the motion scene address of the target motion scene, check whether there is a matching motion scene address in the historical motion scene information. If so, execute step S305 to obtain the fitness equipment owned by the motion scene corresponding to the target motion scene as the target fitness equipment; if not, execute step S306 to scan and identify the target fitness equipment owned by the target motion scene.

[0072] In some embodiments, if the motion scene address of the target motion scene is the user's home address, steps S303 - S304 may not be executed, and step S306 may be directly executed to scan and identify in real time the target fitness equipment owned by the target motion scene and its corresponding placement position. This is because the fitness equipment and placement position inside the user's home are easily affected by the user himself and change, and the accuracy and timeliness of data cannot be guaranteed by obtaining through historical records. However, by scanning and identifying in real time the fitness equipment and placement position inside the user's home, the accuracy of data acquisition can be guaranteed.

[0073] S305: Based on the historical motion scene information, obtain the fitness equipment owned by the motion scene corresponding to the target motion scene as the target fitness equipment.

[0074] According to the motion scene address of the target motion scene, extract the fitness equipment owned by the corresponding motion scene in the historical motion scene information as the target fitness equipment.

[0075] S306: Scan and identify the target fitness equipment owned by the target motion scene.

[0076] Specifically, first, perform an omnidirectional scan of the target motion scene through the camera built into the wearable device or the camera on the user's mobile terminal to obtain a scanned image.

[0077] Next, use the pre - trained fitness equipment recognition model to identify the target fitness equipment in the scanned image and label the identified target fitness equipment in the scanned image. This model covers the image feature data of various common fitness equipment on the market and can quickly and accurately identify key information such as the equipment outline, shape, and color in the picture, and then determine the type of equipment.

[0078] Then, obtain the placement position information of the fitness equipment based on the marked scanned image. Using the coordinate positioning algorithm in image recognition technology, establish a two-dimensional coordinate system with the upper left corner of the scanned image as the origin, and identify the relative coordinate positions of each target fitness equipment in this coordinate system. For example, if a treadmill is located at the 100th pixel horizontally and the 200th pixel vertically in the image, the system will record this coordinate information. At the same time, perform conversion in combination with the actual size ratio of the target motion scene, and convert the pixel coordinates into physical coordinates in the actual space, such as units of meters, centimeters, etc., so as to accurately determine the actual placement position of the equipment in the real scene.

[0079] Finally, construct a layout diagram of the fitness equipment in the target motion scene. Using professional drawing software, draw the icons of each fitness equipment on the layout diagram according to their corresponding physical coordinates to generate a visual scene layout. At the same time, obtain the real-time position information of the user through the positioning module and mark the real-time position information of the user in the layout diagram.

[0080] When the user needs to replace the fitness equipment, the system can directly determine the fitness equipment to be replaced according to the current exercise prescription. Then, determine the relative positions of the user and the fitness equipment based on the fitness equipment to be replaced, the position of the fitness equipment in the layout diagram, and the current position of the user in the layout diagram. Then, plan the route through the route planning algorithm. Finally, according to the planned route, guide the user to find the fitness equipment through voice information. Reduce the time for the user to find the fitness equipment and improve the exercise efficiency.

[0081] S307. When the motion scene type of the target motion scene is outdoor, obtain the motion posture of the user and the pressure information of the user's shoe sole.

[0082] Specifically, obtain the current user's motion posture through a wearable device with multiple sensors worn on the user. The wearable device integrates advanced microelectromechanical system (MEMS) sensors, including accelerometers, gyroscopes, and high-precision magnetometers. The accelerometer can sensitively sense the acceleration changes in various directions when the user's limbs move; the gyroscope can track the rotation angle and angular velocity of the limbs; the magnetometer assists in calibrating the direction to ensure the accuracy of the motion posture data.

[0083] At the same time, obtain the pressure information of the user's shoe sole through an ultra-thin flexible pressure sensor array embedded in the user's shoe sole. These pressure sensors have extremely high sensitivity and can sense the pressure changes generated at the moment when different areas of the shoe sole come into contact with the ground in real time.

[0084] S308. Based on the motion posture and pressure information, determine whether the user is carrying fitness equipment.

[0085] If so, perform the steps of S309; if not, perform the steps of S310.

[0086] Specifically, a typical motion posture feature library and a sole pressure change feature library corresponding to when the user carries different fitness equipment are pre-constructed inside the system. These feature libraries are constructed based on the user's motion posture information and sole pressure information collected when the user historically carried and used various fitness equipment.

[0087] Compare and analyze the currently obtained user motion posture information with the posture features in the feature library, and at the same time match the obtained user sole pressure information with the pressure change features in the feature library. By comprehensively analyzing the matching degree of the motion posture and pressure information with the corresponding features in the feature library, it is determined whether the user is carrying fitness equipment. If the matching degree reaches the preset determination threshold, it is determined that the user is carrying fitness equipment; if the matching degree does not reach this determination threshold, it is determined that the user is not carrying fitness equipment.

[0088] S309. Based on historical equipment usage data, motion postures, and pressure information, determine the fitness equipment carried by the user as the target fitness equipment.

[0089] Among them, the historical equipment usage data includes historical fitness equipment, the motion postures when the user carried and used the historical fitness equipment, and the pressure information of the sole, etc.

[0090] Specifically, first analyze and obtain from the historical equipment usage data the motion postures and the pressure change information of the sole when the user walked while carrying the equipment before each time using the corresponding fitness equipment for exercise. Retrieve the user's historical equipment usage data from the database, and then deeply analyze the historical equipment usage data to determine various historical fitness equipment used by the user in the past, such as barbells, dumbbells, sandbags, skipping ropes, etc. Then, based on the motion postures and the pressure information of the sole when the user carried and used the historical fitness equipment, analyze and obtain the fluctuation of the motion postures of the user when walking while carrying the equipment and the pressure of different regions of the sole changing with time before each time using the corresponding fitness equipment for exercise.

[0091] Next, analyze the currently obtained motion posture information to obtain data such as the range of motion, motion frequency of the limbs, and the flexion and extension states of each joint. At the same time, analyze the current sole pressure information, obtain the specific numerical values of the pressure at different time points and the pressure distribution ratios of different regions, and determine the fluctuation of the pressure of different regions changing with time.

[0092] Then, compare the analyzed data with the fluctuation of the motion postures of the user when walking while carrying various historical fitness equipment and the pressure of different regions of the sole changing with time sorted out before, and comprehensively analyze the matching degree of each item of data on the features corresponding to different historical fitness equipment.

[0093] When the matching degree of a certain historical fitness equipment in terms of the movement posture and the change information of the sole pressure, etc. with the current actual situation exceeds the preset matching degree threshold, determine the carrying method and the equipment weight when the user carrying the equipment corresponding to the historical fitness equipment with the highest matching degree as the carrying method and the equipment weight of the target fitness equipment carried by the user at present.

[0094] Finally, determine the target fitness equipment according to the carrying method and the equipment weight. Obtain the carryable fitness equipment among the fitness equipment corresponding to the user's home address in the historical movement scene information, and determine whether there is a corresponding carrying method and equipment weight of the carryable fitness equipment that match them according to the carrying method and the equipment weight. If it exists, determine this fitness equipment as the target fitness equipment; if it does not exist, it is determined that the number of fitness equipment carried by the user may be multiple or the user has carried fitness equipment that does not belong to the home fitness equipment, and perform permutation and combination on multiple fitness equipment corresponding to the carrying method of the target fitness equipment among the carryable fitness equipment to obtain each different combination, then calculate the equipment weight of each combination, and match the equipment weight of each combination with the equipment weight of the target fitness equipment to calculate the corresponding matching degree. If the matching degree exceeds the preset matching degree threshold, use the fitness equipment in the combination with the highest matching degree as the target fitness equipment.

[0095] If the matching degree of the historical fitness equipment in terms of the movement posture and the sole pressure information, etc. with the current actual situation does not exceed the preset matching degree threshold or the matching degree of each combination does not exceed the preset matching degree threshold, obtain the fitness equipment input by the user as the target fitness equipment, or scan and identify the target fitness equipment carried by the user, which is not limited here.

[0096] In some embodiments, when the matching degree of the historical fitness equipment in terms of the movement posture and the sole pressure information, etc. with the current actual situation does not exceed the preset matching degree threshold or the matching degree of each combination does not exceed the preset matching degree threshold, other fitness equipment can be added to further determine the target fitness equipment.

[0097] First, simulate adding other common fitness equipment of different types and weights to obtain the corresponding changes in the movement posture and the sole pressure information. Then, compare and analyze these simulated data with the action posture information and the sole pressure information actually obtained currently, and calculate the fitness degree value in each case of simulating the added equipment. Then, according to the comparison result of the fitness degree, find out the case of simulating the added equipment with the fitness degree exceeding the preset threshold and the highest fitness degree, and use the fitness equipment corresponding to the simulation case as the target fitness equipment.

[0098] When there are multiple cases where the fitness equipment that is newly added and not significantly recorded in the historical equipment usage data shows a high degree of fit during simulation, this type of new equipment is incorporated into subsequent regular analysis and judgment. At the same time, the corresponding historical usage data is supplemented and collected to improve the entire judgment system, so as to enhance the ability to accurately determine the fitness equipment carried by the user in similar complex situations where it is difficult to match.

[0099] At the same time, other auxiliary information can also be combined, such as the characteristics of the exercise scene where the user is located (for example, if in a park, it may be more inclined to portable small equipment, etc.), and the current time (such as whether it is a period not long after purchasing new equipment, etc.), to further corroborate the speculation about the fitness equipment carried by the user obtained through simulation, making the final judgment result more reliable and in line with the actual situation.

[0100] S310. Determine the preset fitness equipment as the target fitness equipment.

[0101] According to the target exercise scene, determine the preset fitness equipment corresponding to the target exercise scene, and use the preset fitness equipment as the target fitness equipment.

[0102] Among them, the preset fitness equipment is the basic fitness equipment owned by the target exercise scene itself and is stored in the preset exercise scene - basic fitness equipment correspondence table. For example, the preset fitness equipment commonly seen in parks may include horizontal bars, parallel bars, space walkers, waist twisters, etc., which are all basic fitness facilities equipped for the public in parks as public exercise places.

[0103] S311. Obtain the noise decibel range around the user.

[0104] Specifically, noise data is obtained by placing miniature sound sensors on or around the user. These miniature sound sensors have high sensitivity and can accurately capture various sound signals in the surrounding environment. After obtaining the noise data, the signal processing module analyzes and processes the electrical signals transmitted by the sensors, converts them into corresponding decibel values, and continuously monitors for a period of time (for example, recording once every 1 second), and statistically calculates key indicators such as the maximum value, minimum value, and average value of the noise decibels during this period, thereby determining the noise decibel range around the user.

[0105] In some embodiments, if in a relatively complex or large - scale exercise scene (such as a large outdoor stadium, etc.), multiple distributed sound sensors can also be used. The sound data is collected synchronously by multiple sensors, and then the multiple collected data is comprehensively analyzed to more comprehensively and accurately obtain the noise decibel range of the entire surrounding environment, avoiding data deviation caused by problems such as monitoring dead - angles of a single sensor.

[0106] S312. When the type of the target motion scenario is an indoor public sports venue, determine whether the current motion time point is within a preset time range.

[0107] Based on the target motion scenario, determine the preset time range corresponding to the target motion scenario, obtain the current motion time point through the built-in clock module, and determine whether the current motion time point is within the preset time range. If so, execute step S313; if not, execute step S315.

[0108] Among them, the preset time range is the time range suitable for carrying out sports activities. For example, the normal business hours of a gym.

[0109] S313. When the maximum decibel value in the noise decibel range is less than the preset threshold, adjust the initial exercise prescription based on the first preset noise decibel range, the target motion scenario information, and the target fitness equipment to obtain the current exercise prescription.

[0110] Among them, the first preset noise decibel range is the maximum noise range allowed for the target motion scenario during normal time periods.

[0111] Specifically, based on the initial exercise prescription, determine the initial exercise equipment required for the user to execute the initial exercise prescription, and then determine whether all the equipment in the initial exercise equipment is included in the target fitness equipment.

[0112] If the target fitness equipment includes all the equipment in the initial exercise prescription, determine whether there is a fitness equipment in the target fitness equipment whose minimum noise decibel value is greater than or equal to the maximum decibel value of the first preset noise decibel range according to the preset noise range generated by using various fitness equipment. If so, regard this fitness equipment as the fitness equipment to be replaced; if not, determine the exercise intensity of the initial exercise prescription according to the altitude of the target motion scenario, and then adjust the specific exercise parameters such as the number of exercise groups, the number of times per group, and the rest interval in the exercise prescription based on the exercise effect of the initial exercise prescription and this exercise intensity, so that the exercise intensity in the current exercise prescription is this exercise intensity, and the exercise effect is consistent with the exercise effect of the initial exercise prescription.

[0113] If the target fitness equipment does not include all the equipment in the initial exercise prescription, then regard the fitness equipment in the target fitness equipment whose minimum noise decibel value is greater than or equal to the maximum decibel value of the first preset noise decibel range and the missing fitness equipment as the fitness equipment to be replaced.

[0114] Replace the exercise equipment in the initial exercise prescription according to the fitness equipment to be replaced, the fitness equipment with the lowest noise decibel value in the target fitness equipment being less than the highest decibel value of the first preset noise decibel range, and the corresponding exercise types that can be performed. Then, determine the new exercise intensity and new exercise duration according to the replaced equipment and altitude. Finally, re-plan specific exercise parameters such as the number of exercise sets, the number of times per set, and the rest interval according to the new exercise intensity, new exercise duration, and the exercise effect of the initial exercise prescription, so that the exercise effect of the current exercise prescription is consistent with that of the initial exercise prescription.

[0115] Determine whether the noise generated by the user during exercise according to the current exercise prescription is within the first preset noise range. If not, continue to adjust the current exercise prescription. The noise simulation module simulates the possible noise decibel range generated by the user when performing the current exercise prescription in the target exercise scenario. In the noise simulation module, there are pre-stored typical noise data models corresponding to various exercise items in different scenarios, and these models are obtained based on a large number of actual tests and statistical analyses. Compare the simulated noise decibel range with the first preset noise decibel range. If the highest decibel value of the simulated noise decibel range is lower than the highest decibel value of the first preset noise decibel range, it means that the current exercise prescription is suitable for being executed in the current exercise scenario. Otherwise, continue to adjust the current exercise prescription to obtain the current exercise prescription suitable for being executed in the current exercise scenario.

[0116] S314. In the case where the lowest decibel value of the noise decibel range is greater than the preset threshold, adjust the initial exercise prescription based on the noise decibel range, target exercise scenario information, and target fitness equipment to obtain the current exercise prescription.

[0117] S315. Adjust the initial exercise prescription based on the second preset noise decibel range, target exercise scenario information, and target fitness equipment to obtain the current exercise prescription.

[0118] Among them, the second preset noise decibel range is the maximum noise range allowed for the target exercise scenario outside the normal time period.

[0119] Steps S314 and S314 are similar to the above-mentioned step S313, and the description in step S313 can be referred to, and will not be elaborated here.

[0120] S316. Determine the exercise frequency thresholds for each part of the user's body according to the historical medical records in the user's historical health record.

[0121] Specifically, obtain the detailed information of the diseases the user has suffered from in the past according to the user's historical medical records, such as the disease name, the time of illness, the treatment process, and the recovery situation, etc. Then, according to the recovery situation of the diseases, determine whether there are any diseases that have not been completely cured among all the diseases the user has suffered from.

[0122] If there is one unhealed disease, retrieve the exercise frequency thresholds for each body part corresponding to the current recovery stage of the disease from a professional medical database. The professional medical database covers detailed data on the impact of various diseases on body functions at different recovery stages and the exercise frequency thresholds for each body part recommended by medical experts. If not, determine the exercise frequency thresholds for each body part of the user based on a pre-set corresponding table of age groups and exercise frequency thresholds and the user's age group.

[0123] If there are two or more unhealed diseases, retrieve the exercise frequency thresholds for each body part corresponding to each disease at the current recovery stage from the professional medical database in sequence. Combine the multiple exercise frequency thresholds corresponding to each body part, and select the smallest threshold among the multiple exercise frequency thresholds for each body part as the final exercise frequency threshold for each body part.

[0124] S317. Based on the target exercise scenario information, historical health records, and the current exercise prescription, simulate the exercise frequencies of each body part of the user during the execution of the current exercise prescription.

[0125] Specifically, preprocess the data in the target exercise scenario information, historical health records, and the current exercise prescription, and then input the processed data into the exercise simulation module to simulate the exercise frequencies of each body part of the user during the execution of the current exercise prescription.

[0126] Based on the input processed target exercise scenario information, the exercise simulation module will first construct a corresponding virtual exercise environment model to simulate the impact of the venue space, environmental factors, etc. in the real scenario on the exercise. At the same time, combined with the exercise type and action requirements in the current exercise prescription, use the built-in human kinematics model library to select a matching human exercise model, which can accurately reflect the joint activities, muscle forces, etc. of each body part under different exercise actions.

[0127] Then, the module will integrate the individual health characteristic data in the historical health records into the constructed exercise model to perform personalized adjustment on the model. For example, if the user has a history of old knee injuries, then in the simulation of lower limb exercises, the parameters such as the range of motion and force-bearing conditions of the knee joint will be adjusted accordingly to make it more in line with the user's actual physical condition and ensure that the simulation results are more targeted and accurate.

[0128] Based on the adjusted motion model, the module will gradually simulate the changes in the motion states of various parts of the user's body during the entire exercise process according to the parameters such as exercise intensity, frequency, and time arrangement set in the current exercise prescription. Through complex algorithms of exercise mechanics and physiology, it calculates in real time the motion frequencies of various parts of the human body under each action (such as the flexion and extension times of the joints of the limbs, the torsion frequency of the spine, the contraction and relaxation frequency of the muscles, etc.), and records the corresponding data.

[0129] After completing the simulation of the entire exercise process, the module will organize and summarize the recorded motion frequency data of various parts to obtain the motion frequency results of various parts of the user's body during the execution of the current exercise prescription. These results may be presented in the form of a table; they may also be displayed through visual charts, which are not limited here.

[0130] S318. Determine whether there is a part of the body whose motion frequency exceeds the motion frequency threshold.

[0131] Obtain the motion frequencies of various parts of the user's body after executing the current exercise prescription, and determine whether there is a part of the body whose motion frequency exceeds the motion frequency threshold. If so, perform the steps of S319; if not, perform the steps of S320.

[0132] S319. Adjust the current exercise prescription.

[0133] Specifically, determine the target body part whose motion frequency exceeds the threshold, calculate the frequency by which the target body part exceeds the threshold and the frequency required for other body parts to reach the threshold. According to the calculated frequencies, reduce the number of repetitions of the exercise actions corresponding to the target body part in the current exercise prescription, and increase the number of repetitions of the exercise actions corresponding to other body parts, so as to ensure that the motion frequencies of various parts of the user's body do not exceed the motion frequency threshold during the execution of the current exercise prescription.

[0134] In some embodiments, in order to ensure that the generated current exercise prescription is more suitable for injured users, the generated current exercise prescription can be sent to a sports medicine expert for review, obtain the feedback from the sports medicine expert, and further adjust the current exercise prescription according to the feedback.

[0135] S320. Determine whether the user has a disease that matches the preset disease.

[0136] According to the user's historical medical records, determine the diseases the user has, match the diseases the user has with the preset diseases, and determine whether the matching is successful. If so, perform the steps of S321; if not, perform the steps of S324.

[0137] Among them, the preset diseases are diseases that require long-term drug treatment, including heart disease, diabetes, etc.

[0138] S321. Send the first prompt message to the user.

[0139] Before the user executes the current exercise prescription, send a preset first prompt message to the user's mobile terminal through the communication module, and set up a feedback mechanism to determine that the user has carried the medicine.

[0140] Among them, the first prompt message is used to remind the user to take the medicine.

[0141] S322. Predict the predicted time point when the user needs to take the medicine based on the user's real-time physiological parameters, the current exercise prescription, and the historical health records.

[0142] Specifically, according to the historical health records, determine the specific manifestations and corresponding physiological parameter changes during the onset of past diseases, as well as the previous types of medicines, dosages, and medicine-taking time rules, etc. from aspects such as historical medical records, physical examination reports, and previous medicine-taking records.

[0143] Based on the above data, construct an association model between physiological parameters and medicine needs. This association model deeply mines and analyzes a large amount of data through machine learning algorithms (such as support vector machines, decision trees, neural networks, etc.), uses different specific manifestations during the onset of past diseases as classification labels, and the corresponding physiological parameter changes as feature data. Through continuous training and optimization, it accurately learns the internal logical relationship between physiological parameter changes and medicine needs in different disease states.

[0144] At the same time, obtain the user's real-time physiological parameters through sensors, and analyze the data of the exercise type, exercise intensity, and exercise duration in the current exercise prescription. Then, based on the real-time physiological parameters and the exercise type, exercise intensity, and exercise duration in the current exercise prescription, predict the changes in the user's physiological parameters and their corresponding change time points through the prediction model. This prediction model is constructed based on the user's historical exercise data. The historical exercise data includes detailed records of the user's past participation in various exercises, including the specific type of each exercise, the set exercise intensity at that time, the actual exercise duration, and the corresponding physiological parameter changes in each stage during the exercise process.

[0145] After obtaining the result output by the prediction model, input the predicted changes in each physiological parameter into the association model between physiological parameters and medicine needs, and determine whether medicine needs to be taken according to the output result for each physiological parameter change. If medicine needs to be taken, obtain the change time point corresponding to the physiological parameter change as the predicted time point.

[0146] S323. When the current time point is a time point that is a preset time interval before the predicted time point, send a second prompt message to the user.

[0147] According to the predicted time point, determine the time point that is separated from the predicted time point by a preset time interval before the predicted time point. Set a timing task according to this time point. When the current time point matches this time point, send a preset second prompt message to the user's mobile terminal through the communication module. If during the process from the current time point to the preset time point, the user's real-time physiological parameters still change continuously according to the predicted physiological parameter change situation, then play a strong prompt sound through the speaker to prompt the user to take medicine.

[0148] Among them, the second prompt message is used to prompt the user to take medicine.

[0149] In some embodiments, if after the current time point exceeds the preset time point, it is monitored that the user's real-time physiological parameters exceed the critical value that requires taking medicine and are continuously rising, showing a change trend that is not conducive to health, the system can continuously emit a strong prompt sound to attract the attention of other surrounding users, so as to obtain the help of other surrounding users. At the same time, send a notification message to a preset emergency contact person (such as family members, medical staff, etc.). The notification message includes data such as the specific address where the user is located and the real-time physiological parameters, which is convenient for the emergency contact person to pay attention in time and take corresponding measures.

[0150] S324. Based on the user's daily physiological parameters, daily sleep time, daily sleep quality, current exercise amount, and current time point, calculate the optimal exercise amount required for the user to reach the optimal sleep state.

[0151] Specifically, first determine the reference index range of the optimal physical state corresponding to the user reaching the optimal sleep state according to the user's daily physiological parameters, daily sleep time, and daily sleep quality. Obtain the daily sleep quality corresponding to each time the user sleeps and the physiological parameters corresponding to the daily sleep time, and establish an association model between the physiological parameters and the sleep quality. Based on the established association model, analyze the user's sleep quality under different physiological parameters, and find out the range of physiological parameters corresponding to the best sleep quality as the reference index range of the optimal physical state.

[0152] After that, determine the user's physical recovery ability. Based on the user's historical exercise data, view the records of the user's physiological parameters after each exercise in the historical exercise data, and obtain the physiological parameters corresponding to different time points after each exercise completed by the user. Then, according to the physiological parameters corresponding to different time points, determine the corresponding relationship between the different recovery times and the changes in physiological parameters after the user exercises at different intensities, so as to determine the user's physical recovery ability.

[0153] Next, predict the recovery time after the user completes the exercise. Determine the recovery duration from the time point after the user's exercise to the bedtime. Based on the total exercise duration of the current exercise prescription or the user's historical total exercise duration, determine the exercise duration of the user's exercise. Then, according to the current time point and the exercise duration, calculate the predicted time point after the user completes the exercise. Then, according to this predicted time point and the daily sleep time point, calculate the recovery duration from the time point after the user's exercise to the bedtime.

[0154] Then, based on the user's physical recovery ability, the reference index range of the best physical state, and the recovery duration, determine the predicted physiological parameter range of the user at the predicted time point, so that from the predicted time point until the daily sleep time point, the user's body can recover to the best physical state when reaching the daily sleep time point.

[0155] Finally, according to the predicted physiological parameters and the user's real-time physiological parameters, determine the optimal exercise amount required for the user to reach the best sleep state. Compare the user's current physiological parameters with the predicted physiological parameter range, and calculate the difference in each physiological index that needs to be adjusted. Then, by deeply analyzing the historical exercise data, find the correlation law between the change of physiological parameters and the exercise amount, and build a quantitative relationship model based on this. According to the relationship model, convert the difference in each physiological index into the corresponding change in exercise amount. Finally, comprehensively consider the change in exercise amount corresponding to each index, and calculate the optimal exercise amount through methods such as weighted average.

[0156] S325. During the process of the user executing the current exercise prescription, calculate the user's real-time exercise amount according to the user's real-time exercise data and real-time physiological parameters.

[0157] Among them, the real-time exercise data includes exercise type, exercise intensity, exercise duration, etc. The real-time exercise amount is the exercise amount during the process of the user executing the current exercise prescription.

[0158] Specifically, obtain the user's real-time physiological parameters through sensors, including but not limited to heart rate, blood pressure, respiratory rate, blood oxygen saturation, etc. These physiological parameters can reflect the load situation and adaptation degree of the user's body during exercise.

[0159] Then, according to the current exercise prescription and the user's real-time physiological parameters during exercise, determine the user's real-time exercise data, including but not limited to exercise type (such as running, swimming, cycling, etc.), exercise intensity (such as running pace, swimming stroke frequency, cycling cadence, etc.), and the information such as the duration of exercise.

[0160] Finally, based on a large amount of past motion data, a comprehensive algorithm model is constructed. The collected real-time motion data (specific values corresponding to motion type, intensity, and duration) and real-time physiological parameters (specific values of heart rate, blood pressure, respiratory rate, and blood oxygen saturation) are input into the constructed algorithm model in the format required by the model. The algorithm model performs a comprehensive operation on the input data according to the internally set calculation rules and the weights of various parameters, and finally obtains the real-time exercise amount value of the user at the current moment.

[0161] S326. Determine whether the real-time exercise amount is greater than or equal to the optimal exercise amount.

[0162] Determine whether the real-time exercise amount is greater than or equal to the optimal exercise amount. If so, execute step S327 to send a third prompt message to the user, prompting the user not to overexercise; if not, execute step S325 to continue calculating the user's real-time exercise amount.

[0163] S327. When the real-time exercise amount is greater than or equal to the optimal exercise amount, send a third prompt message to the user.

[0164] Send a preset third prompt message to the user's mobile terminal through the communication module.

[0165] Among them, the third prompt message is used to prompt the user not to overexercise.

[0166] In the embodiment of the present application, by collecting the user's physiological parameters in real time and generating an initial exercise prescription according to the user's real-time physical state, the generated initial exercise prescription can accurately adapt to the user's current physical condition. According to the type of exercise scenario, accurately determine the fitness equipment owned by the exercise scenario in different ways, then determine the appropriate noise range, and dynamically adjust the generated initial exercise prescription in combination with the noise range, fitness equipment, and exercise scenario information, so that the generated current exercise prescription can accurately adapt to the exercise scenario where the user is currently located. After generating the current exercise prescription, simulate the exercise frequency of each part of the user's body during the exercise according to the current exercise prescription, and further adjust the current exercise prescription according to the exercise frequency to ensure that the user will not cause damage to each part of the body after exercising according to the current exercise prescription. When the user needs to take medicine for a long time, remind the user to take the medicine and take the medicine to ensure that the user can maintain a good physical state during the exercise, avoid health problems caused by untimely taking of medicine, and make a good balance between exercise and disease control. Real-time monitor whether the user's real-time exercise amount exceeds the optimal exercise amount, and remind the user to pay attention when the user overexercises, to avoid the user's body being overexcited due to excessive exercise, affecting subsequent sleep, and allowing the body to smoothly enter a good rest state after moderate exercise, forming a virtuous cycle of mutual promotion between exercise and sleep.

[0167] The method for dynamically generating an exercise prescription in the embodiments of the present application has been described above. Below, in combination with the above method for dynamically generating an exercise prescription, the exercise prescription generation system in the embodiments of the present application will be described in detail.

[0168] Please refer to Figure 4 , which is an exemplary hardware structure diagram of the exercise prescription generation system in the embodiments of the present application.

[0169] In some embodiments, the exercise prescription generation system 400 includes a computer device, which may be a terminal device. The computer device includes a processor 401, a memory 402, a sensor module 403, a communication module 404, an input device 405, and an output device 406 connected through a system bus. Among them, the processor 401 of the computer device is used to provide computing and control capabilities. The memory 402 of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database is used to store data. The sensor module 403 of the computer device is used to collect data related to the physiological parameters and exercise postures of the user. The communication module 404 of the computer device is used to send prompt information to the user. The input device 405 of the computer device is used to receive input information from the user and sports medicine experts. The output device 406 of the computer device is used to play prompt information and display exercise prescriptions, etc. When the computer program is executed by the processor 401, it implements the method for dynamically generating an exercise prescription in the embodiments of the present application.

[0170] Those skilled in the art can understand that Figure 4 the structure shown in

[0171] is only a block diagram of some structures related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0172] In some embodiments of the present application, a computer-readable storage medium is provided, including instructions, which can cause the exercise prescription generation system 400 to execute the method for dynamically generating an exercise prescription in the embodiments of the present application when the instructions run on the exercise prescription generation system 400.

[0173] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the various embodiments of the present application.

[0174] In the above embodiments, depending on the context, the term "when..." can be interpreted to mean "if...", or "after...", or "in response to determining...", or "in response to detecting...". Similarly, depending on the context, the phrase "when determining..." or "if detecting (the stated condition or event)" can be interpreted to mean "if determining...", or "in response to determining...", or "when detecting (the stated condition or event)", or "in response to detecting (the stated condition or event)".

[0175] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center in a wired (such as coaxial cable, optical fiber, digital subscriber line) or wireless (such as infrared, wireless, microwave, etc.) manner. The computer-readable storage medium can be any available medium that can be accessed by a computer, or a data storage device such as a server or data center that includes one or more integrated available media. The available medium can be a magnetic medium (such as a floppy disk, hard disk, magnetic tape), an optical medium (such as a DVD), or a semiconductor medium (such as a solid-state drive), etc.

[0176] Those of ordinary skill in the art can understand all or part of the processes in the methods of the above embodiments. These processes can be completed by relevant hardware instructed by a computer program. The program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above method embodiments. The foregoing storage media include: ROM or random access memory RAM, magnetic disks, or optical discs and other media that can store program codes.

Claims

1. A method for dynamically generating an exercise prescription, characterized in that: include: Based on the user's historical health records and real-time physiological parameters, an initial exercise prescription for the user is generated through a machine learning model; The historical health records include historical medical records and daily health monitoring data; the daily health monitoring data include daily physiological parameters, daily sleep time and daily sleep quality; the real-time physiological parameters include heart rate, blood pressure and physical fatigue; Determine target sports scene information of the target sports scene of the user according to the current geographical location information of the user; the target sports scene information includes the sports scene address, the sports scene type, and the environment information around the sports scene; Based on the target sports scene information, determining target fitness equipment owned by the target sports scene; The initial exercise prescription is adjusted according to the target exercise scene information and the target fitness equipment of the user to obtain a current exercise prescription.

2. The method according to claim 1, characterized in that The step of determining target fitness equipment owned by the target sports scene based on the target sports scene information specifically includes: When the sports scene type of the target sports scene is indoor, acquiring historical sports scene information; the historical sports scene information includes the sports scene address, the sports scene type, environmental information around the sports scene, fitness equipment owned by the sports scene, and a layout diagram of the sports scene; Determining whether there is a motion scene address in the historical motion scene information that matches the motion scene address of the target motion scene; If yes, based on the historical sports scene information, obtaining the fitness equipment owned by the sports scene corresponding to the target sports scene as the target fitness equipment; If not, scanning and identifying the target fitness equipment owned by the target sports scene; When the sports scene type of the target sports scene is outdoor, the fitness equipment carried by the user is determined to be the target fitness equipment.

3. The method according to claim 2, characterized in that When the sports scene type of the target sports scene is outdoor, determining the fitness equipment carried by the user as the target fitness equipment specifically includes: Acquire the user's motion posture and the pressure information of the user's shoe sole; Based on the motion posture and the pressure information, determining whether the user carries fitness equipment; If so, based on the historical equipment usage data, the movement posture and the pressure information, the fitness equipment carried by the user is determined to be the target fitness equipment; the historical equipment usage data includes historical fitness equipment, the movement posture and sole pressure information of the user when carrying and using the historical fitness equipment.

4. The method according to claim 1, characterized in that: The adjusting the initial exercise prescription according to the target exercise scene information of the user and the target fitness equipment to obtain the current exercise prescription specifically includes: Obtaining the noise decibel range around the user; In the case where the sport scene type of the target sport scene is an indoor public sports venue, determining whether the current sport time point is within a preset time range; If yes, and the highest decibel value of the noise decibel range is less than a preset threshold, the initial exercise prescription is adjusted based on a first preset noise decibel range, the target sports scene information, and the target fitness equipment to obtain a current exercise prescription; the first preset noise decibel range is the maximum noise range allowed by the target sports scene in a normal time period; If yes, and the lowest decibel value of the noise decibel range is greater than a preset threshold, adjusting the initial exercise prescription based on the noise decibel range, the target exercise scene information, and the target fitness equipment to obtain the current exercise prescription; If not, the initial exercise prescription is adjusted based on a second preset noise decibel range, the target exercise scene information, and the target fitness equipment to obtain the current exercise prescription; the second preset noise decibel range is the maximum noise range allowed for the target exercise scene outside the normal time period.

5. The method according to claim 1, characterized in that After the step of adjusting the initial exercise prescription according to the target exercise scene information of the user and the target fitness equipment to obtain a current exercise prescription, the method further includes: Based on the target exercise scene information, the historical health record and the current exercise prescription, simulate and obtain the exercise frequency of each part of the body of the user during the execution of the current exercise prescription; Determining the exercise frequency thresholds of various parts of the user's body according to the historical medical records in the historical health records of the user; Determine whether the movement frequency of a certain body part exceeds the movement frequency threshold; If so, the current exercise prescription is adjusted.

6. The method according to claim 1, characterized in that After the step of adjusting the initial exercise prescription according to the target exercise scene information of the user and the target fitness equipment to obtain a current exercise prescription, the method further includes: Determining whether the user suffers from a disease matching a preset disease according to the historical medical history in the historical health record of the user; If yes, a first prompt message is sent to the user; the first prompt message is used to remind the user to bring medicine; Based on the real-time physiological parameters of the user, the current exercise prescription and the historical health records, predict a predicted time point when the user needs to take medication; When the current time point is a time point before the predicted time point and separated from the predicted time point by a preset time interval, a second prompt message is sent to the user; the second prompt message is used to prompt the user to take medicine.

7. The method according to claim 1, characterized in that After the step of adjusting the initial exercise prescription according to the target exercise scene information of the user and the target fitness equipment to obtain a current exercise prescription, the method further includes: Calculating the optimal amount of exercise required for the user to achieve an optimal sleep state based on the user's daily physiological parameters, the daily sleep time, the daily sleep quality, the current amount of exercise, and the current time point; In the process of the user executing the current exercise prescription, the real-time exercise amount of the user is calculated according to the real-time exercise data of the user and the real-time physiological parameters; the real-time exercise data includes exercise type, exercise intensity, and exercise duration; the real-time exercise amount is the amount of exercise of the user in the process of executing the current exercise prescription; When the real-time exercise amount is greater than or equal to the optimal exercise amount, a third prompt message is sent to the user; the third prompt message is used to prompt the user not to exercise excessively.

8. An exercise prescription generation system, characterized in that: include: one or more processors and memory; The memory is coupled to the one or more processors, and the memory is used to store computer program codes, wherein the computer program codes include computer instructions, and the one or more processors call the computer instructions to enable the exercise prescription generation system to perform the method according to any one of claims 1 to 7.

9. A computer-readable storage medium storing computer instructions, characterized in that: When the computer instructions are executed on an exercise prescription generating system, the exercise prescription generating system is caused to perform the method according to any one of claims 1 to 7.

10. A computer program product, characterized in that When the computer program product runs on an exercise prescription generating system, the exercise prescription generating system is enabled to perform the method according to any one of claims 1 to 7.

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